Research graph
References from Artificial intelligence in breast cancer imaging: a systematic review of detection, segmentation, explainability, and clinical translation. Local targets link to admitted publications; unresolved targets remain external evidence.
Peeking inside the black-box: a survey on explainable artificial intelligence (XAI)
10.1109/access.2018.2870052 · 2018 · External reference
10.48550/arxiv.2511.19667
10.48550/arxiv.2511.19667 · 2025 · External reference
Dataset of breast ultrasound images
10.1016/j.dib.2019.104863 · 2020 · External reference
Machine learning-enabled medical devices authorized by the US Food and Drug Administration in 2024: regulatory characteristics, predicate lineage, and transparency reporting
10.3390/biomedicines13123005 · 2025 · External reference
Simultaneous detection and classification of breast masses in digital mammograms via a deep learning YOLO-based CAD system
10.1016/j.cmpb.2018.01.017 · 2018 · External reference
YOLO based breast masses detection and classification in full-field digital mammograms
10.1016/j.cmpb.2020.105823 · 2021 · External reference
Structured crowdsourcing enables convolutional segmentation of histology images
10.1093/bioinformatics/btz083 · 2019 · External reference
A case-based interpretable deep learning model for classification of mass lesions in digital mammography
10.1038/s42256-021-00423-x · 2021 · External reference
Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries
10.3322/caac.21834 · 2024 · External reference
The impact of mammographic screening on breast cancer mortality in Europe: a review of observational studies
10.1258/jms.2012.012078 · 2012 · External reference
Breast mass segmentation in ultrasound with selective kernel U-Net convolutional neural network
10.1016/j.bspc.2020.102027 · 2020 · External reference
10.1145/3399715.3399744
10.1145/3399715.3399744 · 2020 · External reference
Artificial intelligence for breast cancer screening in mammography (AI-STREAM): preliminary analysis of a prospective multicenter cohort study
10.1038/s41467-025-57469-3 · 2025 · External reference
Grad-CAM++: improved visual explanations for deep convolutional networks
2017 · External reference
Unresolved reference
2021 · External reference
TransUNet: rethinking the U-Net architecture design for medical image segmentation through the lens of transformers
10.1016/j.media.2024.103280 · 2024 · External reference
The macroeconomic burden of noncommunicable diseases in the United States: estimates and projections
10.1371/journal.pone.0206702 · 2018 · External reference
10.48550/arxiv.1901.11210
10.48550/arxiv.1901.11210 · 2019 · External reference
Improving accuracy and efficiency with concurrent use of artificial intelligence for digital breast tomosynthesis
10.1148/ryai.2019180096 · 2019 · External reference
10.7937/tcia.eqde-4b16
10.7937/tcia.eqde-4b16 · 2021 · External reference
Unresolved reference
2020 · External reference
U-Net breast lesion segmentations for breast dynamic contrast-enhanced magnetic resonance imaging
10.1117/1.jmi.10.6.064502 · 2023 · External reference
AI-based triage and decision support in mammography and digital tomosynthesis for breast cancer screening: a paired, noninferiority trial
10.1038/s41591-026-04277-x · 2026 · External reference
Variability in radiologists' interpretations of mammograms
10.1056/nejm199412013312206 · 1994 · External reference
Breast cancer early detection: a phased approach to implementation
10.1002/cncr.32887 · 2020 · External reference
Radiologists' perspectives on AI integration in mammographic breast cancer screening: a mixed methods study
10.3390/cancers17213491 · 2025 · External reference
Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer
10.1001/jama.2017.14580 · 2017 · External reference
Interval cancer, sensitivity, and specificity comparing AI-supported mammography screening with standard double reading without AI in the MASAI study: a randomised, controlled, non-inferiority, single-blinded, population-based, screening-accuracy trial
10.1016/s0140-6736(25)02464-x · 2026 · External reference
Computational approach to radiogenomics of breast cancer: luminal A and luminal B molecular subtypes are associated with imaging features on routine breast MRI extracted using computer vision algorithms
10.1002/jmri.24879 · 2015 · External reference
Predicting breast cancer molecular subtype with MRI dataset utilizing convolutional neural network algorithm
10.1007/s10278-019-00179-2 · 2019 · External reference
10.1007/978-3-031-08999-2_22
10.1007/978-3-031-08999-2_22 · 2022 · External reference
Unresolved reference
External reference
“Current status of the digital database for screening mammography,”
10.1007/978-94-011-5318-8_75 · 1998 · External reference
A deep learning methodology for improved breast cancer diagnosis using multiparametric MRI
10.1038/s41598-020-67441-4 · 2020 · External reference
Segmentation of whole breast and fibroglandular tissue using nnU-Net in dynamic contrast enhanced MR images
10.1016/j.mri.2021.06.017 · 2021 · External reference
Locally advanced breast cancer: MR imaging for prediction of response to neoadjuvant chemotherapy—results from ACRIN 6657/I-SPY TRIAL
10.1148/radiol.12110748 · 2012 · External reference
The EMory BrEast imaging dataset (EMBED): a racially diverse, granular dataset of 3.4 million screening and diagnostic mammographic images
10.1148/ryai.220047 · 2023 · External reference
Secure, privacy-preserving and federated machine learning in medical imaging
10.1038/s42256-020-0186-1 · 2020 · External reference
Detecting and classifying breast masses via YOLO-based deep learning
10.1007/s00521-025-11153-1 · 2025 · External reference
Diagnostic accuracy, fairness and clinical implementation of AI for breast cancer screening: results of multicenter retrospective and prospective technical feasibility studies
10.1038/s43018-026-01127-0 · 2026 · External reference
Unresolved reference
External reference
Long radiology workdays reduce detection and accommodation accuracy
10.1016/j.jacr.2010.03.004 · 2010 · External reference
Breast mass lesion area detection method based on an improved YOLOv8 model
10.3934/era.2024270 · 2024 · External reference
Gender imbalance in medical imaging datasets produces biased classifiers for computer-aided diagnosis
10.1073/pnas.1919012117 · 2020 · External reference
A curated mammography data set for use in computer-aided detection and diagnosis research
10.1038/sdata.2017.177 · 2017 · External reference
National performance benchmarks for modern screening digital mammography: update from the breast cancer surveillance consortium
10.1148/radiol.2016161174 · 2017 · External reference
Microcalcification discrimination in mammography using deep convolutional neural network: towards rapid and early breast cancer diagnosis
10.3389/fpubh.2022.875305 · 2022 · External reference
10.7937/tcia.d8z0-9t85
10.7937/tcia.d8z0-9t85 · 2022 · External reference
1399 HandE-stained sentinel lymph node sections of breast cancer patients: the CAMELYON dataset
10.1093/gigascience/giy065 · 2018 · External reference
A survey on deep learning in medical image analysis
10.1016/j.media.2017.07.005 · 2017 · External reference
10.1109/iccv48922.2021.00986
10.1109/iccv48922.2021.00986 · 2021 · External reference
Robust breast cancer detection in mammography and digital breast tomosynthesis using an annotation-efficient deep learning approach
10.1038/s41591-020-01174-9 · 2021 · External reference
Current state of mammography-based artificial intelligence for future breast cancer risk prediction: a systematic review
10.1093/jnci/djag002 · 2026 · External reference
Automatic joint segmentation and classification of breast ultrasound images via multi-task learning with object contextual attention
10.3389/fonc.2025.1567577 · 2025 · External reference
Segment anything in medical images
10.1038/s41467-024-44824-z · 2024 · External reference
Missed breast carcinoma: pitfalls and pearls
10.1148/rg.234025083 · 2003 · External reference
Breast MRI: state of the art
10.1148/radiol.2019182947 · 2019 · External reference
International evaluation of an AI system for breast cancer screening
10.1038/s41586-019-1799-6 · 2020 · External reference
INbreast: toward a full-field digital mammographic database
10.1016/j.acra.2011.09.014 · 2012 · External reference
Optimized YOLOv8 for enhanced breast tumor segmentation in ultrasound imaging
10.1007/s12672-025-02889-2 · 2025 · External reference
Unresolved reference
2025 · External reference
VinDr-Mammo: A large-scale benchmark dataset for computer-aided diagnosis in full-field digital mammography
10.1038/s41597-023-02100-7 · 2023 · External reference
Generalizable attention U-Net for segmentation of fibroglandular tissue and background parenchymal enhancement in breast DCE-MRI
10.1186/s13244-023-01531-5 · 2023 · External reference
The PRISMA 2020 statement: an updated guideline for reporting systematic reviews
10.1136/bmj.n71 · 2021 · External reference
Breast cancer detection using mammography: image processing to deep learning
10.1109/access.2024.3523745 · 2024 · External reference
10.1109/cvpr.2016.91
10.1109/cvpr.2016.91 · 2016 · External reference
Unresolved reference
External reference
Detecting and classifying lesions in mammograms with Deep Learning
10.1038/s41598-018-22437-z · 2018 · External reference
The future of digital health with federated learning
10.1038/s41746-020-00323-1 · 2020 · External reference
10.1007/978-3-319-24574-4_28
10.1007/978-3-319-24574-4_28 · 2015 · External reference
Unresolved reference
External reference
A machine learning approach to radiogenomics of breast cancer: a study of 922 subjects and 529 DCE-MRI features
10.1038/s41416-018-0185-8 · 2018 · External reference
10.7937/tcia.e3sv-re93
10.7937/tcia.e3sv-re93 · 2022 · External reference
External evaluation of 3 commercial artificial intelligence algorithms for independent assessment of screening mammograms
10.1001/jamaoncol.2020.3321 · 2020 · External reference
Grouped mask region convolution neural networks for improved breast cancer segmentation in mammography images
10.1007/s12530-023-09527-8 · 2024 · External reference
10.7937/k9/tcia.2016.7o02s9cy
10.7937/k9/tcia.2016.7o02s9cy · 2016 · External reference
Unresolved reference
External reference
“Grad-cam: visual explanations from deep networks via gradient-based localization,”
10.1109/iccv.2017.74 · 2017 · External reference
Data-driven classification and explainable-AI in the field of lung imaging
10.3389/fdata.2024.1393758 · 2024 · External reference
Deep learning in medical image analysis
10.1146/annurev-bioeng-071516-044442 · 2017 · External reference
Deep learning to improve breast cancer detection on screening mammography
10.1038/s41598-019-48995-4 · 2019 · External reference
10.48550/arxiv.1901.05350
10.48550/arxiv.1901.05350 · 2019 · External reference
Unresolved reference
External reference
A dataset for breast cancer histopathological image classification
10.1109/tbme.2015.2496264 · 2016 · External reference
Prevalence of mammographically dense breasts in the United States
10.1093/jnci/dju255 · 2014 · External reference
The evaluation of artificial intelligence in mammography-based breast cancer screening: is breast-level analysis enough?
10.1007/s00330-025-11733-8 · 2025 · External reference
Comprehensive molecular portraits of human breast tumours
10.1038/nature11412 · 2012 · External reference
BUS-Set: a benchmark for quantitative evaluation of breast ultrasound segmentation networks with public datasets
10.1002/mp.16287 · 2023 · External reference
Open-source dataset for the RSNA screening mammography cancer detection challenge
10.1148/ryai.250375 · 2026 · External reference
Attention-enriched deep learning model for breast tumor segmentation in ultrasound images
10.1016/j.ultrasmedbio.2020.06.015 · 2020 · External reference
Computational radiomics system to decode the radiographic phenotype
10.1158/0008-5472.can-17-0339 · 2017 · External reference
“Mammo-SAM: adapting foundation segment anything model for automatic breast mass segmentation in whole mammograms,”
10.1007/978-3-031-45673-2_18 · 2023 · External reference
Toward robust mammography-based models for breast cancer risk
10.1126/scitranslmed.aba4373 · 2021 · External reference
Automated breast ultrasound lesions detection using convolutional neural networks
10.1109/jbhi.2017.2731873 · 2018 · External reference
Clinical application of AI in mammography: insights from a prospective study
10.1016/j.acra.2025.05.025 · 2025 · External reference
CoAtUNet: A symmetric encoder-decoder with hybrid transformers for semantic segmentation of breast ultrasound images
10.1016/j.neucom.2025.129660 · 2025 · External reference
